Deviation based fault detection method for shackles under variable loading

Abstract

Shackles used in lifting work are mainly subjected to fatigue loading during operation. The failure of the shackles can lead to catastrophic accidents and economic loss because they serve as a connection between loads and lifting equipment. Thus, it is necessary to detect the faults of shackles in advance. In this study, weak points of a shackle were identified through the calculated stress distribution and were verified through fatigue tests. The representative features were extracted based on RMS and peak-to-peak values of dual strain data. Thresholds for fault detection were defined using the features and the weight functions considering the inverse proportion between strain values and lifetime of shackles. The performance of detectors was evaluated by comparing with cycles between the detected fault and the incipient crack. The selected detector without using complex formulas can carry out the fault detection of shackles effectively.

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Sunghyun Lee is a Ph.D. candidate in Mechanical Engineering at Chonnam National University, Republic of Korea. He is also studying at the Reliability Assessment Center at Korea Institute of Machinery and Materials. His research interests include fault detection and prognostics of the mechanical system.

Insu Jeon received his Ph.D. in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST) at 2000. He joined the School of Mechanical Engineering at Chonnam National University in 2006 as a faculty member. He has interests in fracture mechanics, biomechanics and biomaterials.

Dong-Cheon Baek is a Senior Researcher at the Reliability Assessment Center at the Korea Institute of Machinery and Materials (KIMM). He received his B.S., M.S. and Ph.D. (2009) in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST). His research interest is in prognostics and health management (PHM) of the mechanical system.